The present work aims to close this gap by comparing the performance of state-of-the-art deep neural networks in different challenging face recognition environments in a reproducible, comparable and extensible manner.
To achieve this, four pre-trained networks from the state of the art are examined.
Experiments are performed on six datasets, including AR face [1]}, Multi-PIE [2]}, SCface [3]}, GBU [4]}, IJB-C [5]}, and MOBIO [6]}, each of which represents a different aspect of face recognition.
Our implementations of evaluation protocols [7]}, [8]} allow the isolated consideration of different aspects of face variations, such as different types of occlusion, facial expressions, or poses.
The performed experiments make use of the open-source biometrics recognition pipeline [9]} of Bob [10]}, [11]}.
Together with this study, an open-source implementation to re-run (or at least re-evaluate) the experiments is provided.https://gitlab.idiap.ch/bob/bob.paper.8years
